WLR Tutorial

1. We load the CITE-seq human PBMC data. You can follow the Seurat tutorial or download the analyzed R object.

2. We run WLR on the RNA data.

ptm <- proc.time()
cbmc_rna <- RunWLR(object = cbmc_rna, n_iters = 1000, wlr_pca_dims = 20, wlr_k = 30, ratio = 0.1, norm_type = "l1")

Run Bootstrapping
Finalize

ptm1 <- proc.time() 
print(x = ptm1 - ptm)

   user  system elapsed 
 42.979  13.618  70.487 

3. We run WLR on the ADT data.

print(x = ptm1 - ptm)

   user  system elapsed 
 34.798  11.520  58.947 

4. We fuse the RNA and ADT data. We see that CD4+ and CD8+ T cells are more defined by the ADT data in the surface protein space, while Dendritic cells (DCs) are more defined in the RNA-seq data due to the absence of its surface marker in the ADT data.

5. We perform UMAP dimensional reduction on the fused data. Additionally, clustering can be performed as well.